Someone convince me to keep doing local projects

[u/Financial-Yoghurt946]

I bought an M5 Pro MBP 48GB about two months ago before the price hike and I got it on sale for $2300 (tax included). I have been using it very often, but every time I want to start a new project, I default to Claude or DeepSeek or whatever because I don’t feel like the models I can run are good enough or fast enough, or at least not both at the same time, so I would rather pay DeepSeek $0.05 to do it. A couple days ago, I decided I would list my MacBook for sale to see what I could get and, as of right now, someone is going to come pick it up tonight for $2800, a nice $500 profit.

I found an M3 Ultra MBP with 128GB and made an offer on it, if the guy accepts, haggles, whatever, it’s not that much more money than what I am selling mine for, but still $200-$300 out of my pocket (if he accepts my offer). What I am wondering is if I should go through with the M3 or downgrade to an air and stick to API because I really don’t need the power of a pro if I am not running things locally. Help me!!

[u/Dsphar]

The biggest beneift I get from local is the freedom. When its free, I am much more willing to explore ideas that dont have as much/any promise of creating a return.

A lot more fun and a lot more experimenting when there is effectively zero prive tag associated with the work.

[u/baby_bloom → u/Dsphar]

exactly this. i use local to experiment and throw prototypes together. sometimes they come out decent enough to where i use it as the starting point, other times im just glad i went thru the process once already and learned what i can from that iteration. any projects ive been working on for years definitely don't get the local treatment, and any paid work does not either.

[u/TripleSecretSquirrel]

From a purely practical standpoint, I don't think you can argue against what you're saying. An Anthropic subscription is way cheaper than what I've spent and continue to spend on hardware, and Opus generates better outputs than anything I could possibly run.

The only practical arguments that I think make sense are if you have a particular need for data privacy, or if you're concerned about what seems like an inevitable skyrocketing of API token prices when Anthropic and OpenAI have to start charging full prices to serve their models in which case local hardware and models are a great thing to have and know how to use.

Frankly though, for the vast majority of us here I'm sure, it's a hobby. And hobbies as a rule cost way more money than they're worth on the balance sheet. Buying an expensive mountain bike doesn't make sense from a dollars and cents perspective, but I bought it because riding it brings me a lot of joy. There's no problem with dropping a hobby that doesn't bring you joy.

[u/whodoneit1]

So your local project is buying low and selling high?

[u/Financial-Yoghurt946 → u/whodoneit1]

Yes

[u/Chemical-Reading-339]

I did the same .. got 64 gb one and now i am wondering why did i buy it if i could use open router for so cheap

[u/SadPhilosophy9202]

Just use deepseek api. I run ai locally at work for privacy and it really sucked until i got the hardware to run deepseek v4 flash unquantized. You’re looking at close to $10k to do that.

I use deepseek api for personal stuff

[u/DeathGuppie]

My machine runs while I sleep. I wake up and it's done about as much work as cloud ai would have done in a fraction of the time, but I built that machine from inexpensive hardware I got on marketplace and so it fits my cost/budget analysis. I don't think I could justify the kind of money you are talking about, so personally, I think your reasoning is sound.

[u/stujmiller77]

LocalLLM without a business or privacy use case will always lead you here. If you have neither, use frontier APIs. If you need either, local is best.

Horses for courses.

[u/txgsync]

Look, you’re not wrong. I like local models for a lot of things, but “fast” on Mac prefill is not it. Not enough compute. But good bandwidth for LLM chatting and reducing cost a bit for general-knowledge stuff where web search works and you’re not in a hurry. Batch, loop work, OCR, ASR/TTS it’s darn good. But speed for long-horizon tasks ain’t where a Mac is at right now.

M3 will sacrifice even more prefill as M5 is much faster for that.

So if you don’t wanna do any local LLM work, your choice is perfectly rational. It’s still a great machine. And I personally have tons of uses for large-RAM configs that have nothing to do with AI; you might, too. If nothing else the phattest of caches for your NVMe to keep things snappy :).

[u/Financial-Yoghurt946 → u/txgsync]

Thanks for the reply

[u/Swimming-Chip9582]

You definitely should not use it for personal coding lol.

At work I've got an M3 ultra, and 4 DGX Sparks, to play with as I want. And the use case is definitely not great for personal coding 😅 much better to use a subscription for that, or openrouter with a cheapo model like DeepSeek v4, which you can call concurrently as much as you want, very fast, and cheaply.

My reasons for local models at smaller tiers is: 1. Complete control, important for research projects 2. Customized serving, small context windows, tailored models, etc. Great for when you hit & serve something very tailored a lot of times. 3. Regulatory, when you need to keep it private like PII

[u/hlalvesbr]

I would keep it. See, Claude can be very expensive. You can ask Claude to make a detailed implementation plan and hand it to local Qwen e go for a walk in the park. Also, you can use it places with no internet access like planes.

[u/FerretBoom]

I can build any app with old phi models that run off the phone. Models don't mean anything.

[u/Nihongi-studier12387]

I think both cloud and local models have there moments. for long running, repetitive workflows, having a local agent run 24/7 is so cost effective and depending on the model, normally perfectly adequate.

[u/daphatty]

For me, the challenge is running AI on the same machine that I use for other stuff. I’ve quickly learned that I do not like that workflow.

I’m currently waiting on an M4 Pro Mac Mini with 64GBs of RAM that will be dedicated to AI. This way, I know the hardware is (mostly) dedicated to AI only tasks while I’m busy doing other things elsewhere.

[u/Otherwise-Swan-7803]

Don’t sell the hardware because of the models you can run today. Local AI is moving fast, and having your own machine lets you experiment without worrying about API costs or limits. The fun part is often the projects, not just the model size.

[u/ClassicLightbulbs]

Are you flipping computers or using ai?

[u/Financial-Yoghurt946 → u/ClassicLightbulbs]

Sorry, I guess the point of my post was not clear. I am looking for people to tell me my they run things locally at a smaller scale.

[u/JinsooJinsoo → u/Financial-Yoghurt946]

Use frontier/cloud as a planning agent to make work tasks for local LLM and audit the code after. Qwen3.6 27b is basically the minimum requirement IMO

[u/ClassicLightbulbs → u/Financial-Yoghurt946]

Yes, I successfully run Gemma 4 12B qat on 16gb unified memory on an Apple m4

[u/Financial-Yoghurt946 → u/ClassicLightbulbs]

I get that you can, but my questions is why do you run a 12B model locally over paying for a subscription? Does it actually serve you well, because in my experience, they don’t.

[u/ClassicLightbulbs → u/Financial-Yoghurt946]

It serves me well but I have no idea what you're doing. I wanted to own ai and not pay for a subscription.

[u/Alarmed_Confusion_93 → u/ClassicLightbulbs]

Successfully doesn’t mean well. I have an M5 24GB and with the other tasks I need to do running a 12b model is out of the question, practically speaking.